{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from malaya.text.function import (\n",
    "    check_ratio_numbers,\n",
    "    check_ratio_punct,\n",
    "    is_emoji,\n",
    "    is_laugh,\n",
    "    is_mengeluh\n",
    ")\n",
    "import malaya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Load pretrained wordvector into `malaya.wordvector.WordVector` class will disable eager execution.\n",
      "2023-06-03 13:30:39.937838: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-03 13:30:39.980921: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2023-06-03 13:30:39.980946: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: husein-MS-7D31\n",
      "2023-06-03 13:30:39.980949: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: husein-MS-7D31\n",
      "2023-06-03 13:30:39.981015: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.182.3\n",
      "2023-06-03 13:30:39.981031: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 470.182.3\n",
      "2023-06-03 13:30:39.981033: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 470.182.3\n"
     ]
    }
   ],
   "source": [
    "vocab, embedded = malaya.wordvector.load(model = 'socialmedia')\n",
    "wordvector = malaya.wordvector.WordVector(embedded, vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "nn = NearestNeighbors(n_neighbors=31, metric='cosine').fit(\n",
    "    wordvector._embed_matrix\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jokowi\n",
      "prabowo\n",
      "soekarno\n",
      "soeharto\n",
      "wiranto\n",
      "anjeng\n",
      "goblog\n",
      "kontol\n",
      "pakde\n",
      "bisa\n",
      "ngasilin\n",
      "ngapain\n",
      "gemer\n",
      "partai\n",
      "gue\n",
      "uang\n",
      "kalo\n",
      "pake\n",
      "kampret\n",
      "ngubah\n",
      "antek\n",
      "aseng\n",
      "brengsek\n",
      "pakbis\n",
      "input not found in dictionary, here top-5 nearest words [pakis, pakbin, pakbim, pakboi, pakbos]\n",
      "ngehe\n",
      "kontol\n",
      "bodoamat\n",
      "walikota\n",
      "donk\n",
      "pamer\n",
      "ngapa2in\n",
      "tiduran\n",
      "ngomong\n",
      "disholatin\n",
      "bngt\n",
      "bangkeknye\n",
      "input not found in dictionary, here top-5 nearest words [bangkenye, bangkek, bangkee, bangkenya, bangkekk]\n",
      "brengsek\n",
      "pakbis\n",
      "input not found in dictionary, here top-5 nearest words [pakis, pakbin, pakbim, pakboi, pakbos]\n",
      "ngehe\n",
      "konyol\n",
      "cewe\n",
      "segede\n"
     ]
    }
   ],
   "source": [
    "words = ['jokowi', 'prabowo', 'soekarno', 'soeharto', 'wiranto', 'anjeng', 'goblog', 'kontol',\n",
    "        'pakde', 'bisa', 'ngasilin', 'ngapain', 'gemer', 'partai', 'gue', 'uang', 'kalo',\n",
    "        'pake', 'kampret', 'ngubah', 'antek', 'aseng','brengsek', 'pakbis', 'ngehe', 'kontol',\n",
    "        'bodoamat', 'walikota', 'donk' ,'pamer', 'ngapa2in', 'tiduran',\n",
    "        'ngomong', 'disholatin', 'bngt', 'bangkeknye', 'brengsek', 'pakbis', 'ngehe',\n",
    "        'konyol', 'cewe', 'segede']\n",
    "\n",
    "similars = []\n",
    "for word in words:\n",
    "    print(word)\n",
    "    \n",
    "    try:\n",
    "        distances, idx = nn.kneighbors(\n",
    "            wordvector.get_vector_by_name(word).reshape((1, -1))\n",
    "        )\n",
    "        word_list = []\n",
    "        for i in range(1, idx.shape[1]):\n",
    "            word_list.append(\n",
    "                [wordvector._reverse_dictionary[idx[0, i]], 1 - distances[0, i]]\n",
    "            )\n",
    "        similars.append(word_list)\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://raw.githubusercontent.com/ardwort/freq-dist-id/master/data/twitter.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "vocab_files = glob('indonlu/dataset/*/vocab_uncased.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabs = []\n",
    "for f in vocab_files:\n",
    "    with open(f) as fopen:\n",
    "        v = fopen.read().split('\\n')\n",
    "    vocabs.extend(v)\n",
    "vocabs = list(filter(None, vocabs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import enchant\n",
    "d = enchant.Dict('en_US')\n",
    "\n",
    "is_english = lambda x: d.check(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>di</td>\n",
       "      <td>404300</td>\n",
       "      <td>1.162798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yg</td>\n",
       "      <td>279458</td>\n",
       "      <td>0.803743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ya</td>\n",
       "      <td>270836</td>\n",
       "      <td>0.778945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aku</td>\n",
       "      <td>250220</td>\n",
       "      <td>0.719652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yang</td>\n",
       "      <td>240116</td>\n",
       "      <td>0.690592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0       1         2\n",
       "0    di  404300  1.162798\n",
       "1    yg  279458  0.803743\n",
       "2    ya  270836  0.778945\n",
       "3   aku  250220  0.719652\n",
       "4  yang  240116  0.690592"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('twitter.csv', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reject(w):\n",
    "    if malaya.dictionary.is_malay(w):\n",
    "        return True\n",
    "    if malaya.dictionary.is_english(w):\n",
    "        return True\n",
    "    if is_english(w):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def reject_vocab(w):\n",
    "    try:\n",
    "        if check_ratio_numbers(w) > 0.4:\n",
    "            return True\n",
    "        if check_ratio_punct(w) > 0.4:\n",
    "            return True\n",
    "        if is_emoji(w):\n",
    "            return True\n",
    "        if is_laugh(w):\n",
    "            return True\n",
    "        if is_mengeluh(w):\n",
    "            return True\n",
    "        if w.startswith('</') and w.endswith('>'):\n",
    "            return True\n",
    "        if reject(w):\n",
    "            return True\n",
    "        return False\n",
    "    except:\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = set(df[0].dropna().tolist())\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2855"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indon_words = [w for w in df if not reject_vocab(w)]\n",
    "len(indon_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50632"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_indon = [w for w in vocabs if not reject_vocab(w)]\n",
    "len(vocab_indon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "_list_laughing = {\n",
    "    'huhu',\n",
    "    'haha',\n",
    "    'gaga',\n",
    "    'hihi',\n",
    "    'wkawka',\n",
    "    'wkwk',\n",
    "    'kiki',\n",
    "    'keke',\n",
    "    'huehue',\n",
    "    'hih',\n",
    "    'waka',\n",
    "    'ckck',\n",
    "    'hehe',\n",
    "    'lolol',\n",
    "    'lol',\n",
    "    'wkaka'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2363"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "import re\n",
    "\n",
    "merged = list(itertools.chain(*similars))\n",
    "indon_socialmedia_words = set([i[0] for i in merged if len(i[0]) > 2 and not len(re.findall(r'\\d+',i[0]))])\n",
    "indon_socialmedia_words = indon_socialmedia_words | set(words) | set(indon_words) | df\n",
    "indon_socialmedia_words = {i for i in indon_socialmedia_words if type(i) == str}\n",
    "indon_socialmedia_words = {i for i in indon_socialmedia_words if len(i) > 2}\n",
    "indon_socialmedia_words = {\n",
    "        word\n",
    "        for word in indon_socialmedia_words\n",
    "        if not any([laugh in word for laugh in _list_laughing])\n",
    "        and word[: len(word) // 2] != word[len(word) // 2 :]\n",
    "}\n",
    "\n",
    "minus = list([w for w in indon_socialmedia_words if not reject_vocab(w) and len(w) > 3 and w[-1] != '2'])\n",
    "len(minus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "rejects = ['yup','babi', 'tanda','blajar', 'aah','iyaa','mana','smbil','knpa',\n",
    "          'adekku', 'bigbang','masa','dropbox','scumbag',\n",
    "          'wakaka', 'hmmm', 'emoticon','mana', 'bangun','ehm',\n",
    "          'sia','ehh', 'mey', 'marah','isn','duh','eek',\n",
    "          'akuu','twiter','twitter','ferrari','hahha',\n",
    "          'mlayu','woyy','ost','yaa','lgs','otw','okeee',\n",
    "          'waah','waaah','iyaa','eee','berenti','iphone',\n",
    "          'mbayar','mbahas','brenti','tanya','yeee','ayooo','kaaak',\n",
    "          'hooh','haha','hha','yaaa','faisal','pliss','rvp',\n",
    "          'ciee','fifa','kkk','argh','tweetnya','oon','lagi',\n",
    "          'loooh','looh','everytime','10rb','gaza',\n",
    "          'entahlah','amin','gile','crita','besok',\n",
    "          'aaakk','singap','lama','kemana','jangan',\n",
    "          'usb','moh','eeh','gsm','nihh','amin',\n",
    "          'haaa','akhir','nego','bljr','bts',\n",
    "          'jeh','kih','xdd','guw','adele','uhuy',\n",
    "          'apa','bgitu','amiin','amiiin','mantab',\n",
    "          'bored','tmpt','skt','tau','laen','tidor',\n",
    "          'tuhh','cari','sblm','baca','setaun','tmbh',\n",
    "          'mkan','wassap','innali','htc','zzz','ahhh',\n",
    "          'banyak2','mancing','main','jgn','lohh',\n",
    "          'itu','jauh','ha3','hugs','anj','ntah',\n",
    "          'aww','kfc','pkai','kawan','tdi',\n",
    "          'dulu','kali2','oww','acc','waa','dtg',\n",
    "           'bgn','fyi','inii','yey','yah',\n",
    "          'lgi','syg','eaa','bnyk','ngaku2',\n",
    "          'wtf','bukak','hii','pfft','obama','aaak',\n",
    "          'mmm','hbo','yeay','huaa','tlg','malu','avenger',\n",
    "          'knp','php','oppa','broo','hus','sucks','weh','rmh','bln','cmn','wib','ihh',\n",
    "          'ics','hardisk','ooh','yooo','beres','yamaha','seo','smua','mauu','pagi',\n",
    "          'satu2','heu','hhe','skrng','err', 'ronaldo','cita','omg','socmed',\n",
    "          'ktm','uitm','nyet','ahh','dkt','ikut','klau','nikmat','guling','gpp',\n",
    "          'ubuntu','krja','fav','bkn','makan','uhuk','pesbuk','slalu','retweets',\n",
    "          'xperia','smoga','messi','friendzone','soundcloud','bagi', 'bro',\n",
    "          'spiderman', 'unfollow', 'mozilla', 'beatles', 'sama2',\n",
    "          'agak2', 'sayangku', 'ɔˇ³ˇ','º̩̩̀', 'untungnya', 'ketawanya', 'spain',\n",
    "          'istri', '̩̩̩͡', 'sony', 'hadiahnya', 'laki2', 'coldplay', 'italy',\n",
    "          'linux', 'watsap','woiii','orang2', 'jalan2', 'hati2', 'farah', 'dslr', 'dihatimu',\n",
    "          'linkedin', 'rata2', 'dadah2', '̮ơ̴̴͡', 'ɨɨɑɨɨɑɨɨɑ', 'nobita', 'disini', 'kaan',\n",
    "          'sekalii', 'endingnya', 'tapinya']\n",
    "\n",
    "minus = [m for m in minus if all([r not in m for r in rejects])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "minus.extend(['takbisa', 'ngapen', 'paskabayar', 'kok', 'sampe', 'asin', 'gurih', 'sih',\n",
    "             'buk', 'neng', 'uang', 'pacar', 'jasmu', 'ujung', 'telen', 'banget',\n",
    "             'bisa', 'pengen', 'gue', 'wong', 'ganteng', 'gwe', 'apaan', 'dengerinnya',\n",
    "             'dengerinya', 'dengerin', 'fonsel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(minus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.dictionary.is_malay('dijalan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dijalan',\n",
       " 'mwalfin',\n",
       " 'karokean',\n",
       " 'colokan',\n",
       " 'kode',\n",
       " 'iyoo',\n",
       " 'weleh',\n",
       " 'sante',\n",
       " 'moso',\n",
       " 'emaknya',\n",
       " 'kosannya',\n",
       " 'temenku',\n",
       " 'ndusel',\n",
       " 'namamu',\n",
       " 'iseh',\n",
       " 'jogjakarta',\n",
       " 'dicek',\n",
       " 'palestina',\n",
       " 'ngok',\n",
       " 'nabrak',\n",
       " 'ngebut',\n",
       " 'kalibata',\n",
       " 'ndue',\n",
       " 'patrakars',\n",
       " 'hajar',\n",
       " 'durung',\n",
       " 'kantong',\n",
       " 'enggres',\n",
       " 'utang',\n",
       " 'beib',\n",
       " 'dibilangin',\n",
       " 'ngepet',\n",
       " 'ngarti',\n",
       " 'wedok',\n",
       " 'dimaksud',\n",
       " 'bagusan',\n",
       " 'jelasin',\n",
       " 'ndah',\n",
       " 'sitik',\n",
       " 'seriusan',\n",
       " 'ujannya',\n",
       " 'anying',\n",
       " 'couldn',\n",
       " 'onlen',\n",
       " 'yaiyalah',\n",
       " 'mboten',\n",
       " 'keur',\n",
       " 'pfffttt',\n",
       " 'glek',\n",
       " 'santet',\n",
       " 'mentri',\n",
       " 'mbak',\n",
       " 'godain',\n",
       " 'kapolri',\n",
       " 'kelaperan',\n",
       " 'keringet',\n",
       " 'mboh',\n",
       " 'patjar',\n",
       " 'fatmawati',\n",
       " 'nggilani',\n",
       " 'rene',\n",
       " 'bebeb',\n",
       " 'ndang',\n",
       " 'jenenge',\n",
       " 'solusinya',\n",
       " 'denpasar',\n",
       " 'ndra',\n",
       " 'aceng',\n",
       " 'krik',\n",
       " 'ksana',\n",
       " 'yutub',\n",
       " 'ngana',\n",
       " 'ospek',\n",
       " 'handphone',\n",
       " 'nomor',\n",
       " 'pemda',\n",
       " 'rezim',\n",
       " 'wisuda',\n",
       " 'minal',\n",
       " 'hayu',\n",
       " 'kesini',\n",
       " 'percoyo',\n",
       " 'maksa',\n",
       " 'teroris',\n",
       " 'keselek',\n",
       " 'ngendus',\n",
       " 'maning',\n",
       " 'pikirin',\n",
       " 'salafi',\n",
       " 'wenger',\n",
       " 'ngising',\n",
       " 'bakwan',\n",
       " 'akutuh',\n",
       " 'ditraktir',\n",
       " 'masnya',\n",
       " 'kada',\n",
       " 'sugeng',\n",
       " 'pemkot',\n",
       " 'rabb',\n",
       " 'rasah',\n",
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       " 'cibubur',\n",
       " 'sebelahan',\n",
       " 'ngebayangin',\n",
       " 'ojek',\n",
       " 'teteh',\n",
       " 'kokoh',\n",
       " 'wasitnya',\n",
       " 'kedepan',\n",
       " 'syuting',\n",
       " 'malioboro',\n",
       " 'vampir',\n",
       " 'idul',\n",
       " 'aidzin',\n",
       " 'pbnu',\n",
       " 'ceweknya',\n",
       " 'pffftt',\n",
       " 'suarez',\n",
       " 'pakde',\n",
       " 'plurk',\n",
       " 'raiso',\n",
       " 'antv',\n",
       " 'heker',\n",
       " 'maaci',\n",
       " 'tawuran',\n",
       " 'pdip',\n",
       " 'ngefek',\n",
       " 'nangkap',\n",
       " 'kalee',\n",
       " 'bunderan',\n",
       " 'justru',\n",
       " 'kamuh',\n",
       " 'indosat',\n",
       " 'ayoo',\n",
       " 'rinduku',\n",
       " 'kepo',\n",
       " 'ncen',\n",
       " 'niscaya',\n",
       " 'kapolres',\n",
       " 'rusak',\n",
       " 'orba',\n",
       " 'prok',\n",
       " 'sanah',\n",
       " 'wonge',\n",
       " 'isin',\n",
       " 'mbayangke',\n",
       " 'bajakan',\n",
       " 'meler',\n",
       " '̩̩̩ƪ',\n",
       " 'keujanan',\n",
       " 'daus',\n",
       " 'kosan',\n",
       " 'eonni',\n",
       " 'yesss',\n",
       " 'moal',\n",
       " 'opini',\n",
       " 'ditawarin',\n",
       " 'wagub',\n",
       " 'como',\n",
       " 'kebelet',\n",
       " 'ngupil',\n",
       " 'eike',\n",
       " 'akeh',\n",
       " 'diblok',\n",
       " 'ngelirik',\n",
       " 'nyium',\n",
       " 'temlen',\n",
       " 'uangnya',\n",
       " 'juve',\n",
       " 'grogi',\n",
       " 'ngutang',\n",
       " 'udahan',\n",
       " 'bakrie',\n",
       " 'juli',\n",
       " 'komplain',\n",
       " 'jarene',\n",
       " 'samarinda',\n",
       " 'andika',\n",
       " 'ndan',\n",
       " 'ndelok',\n",
       " 'dilepas',\n",
       " 'dugem',\n",
       " 'bokap',\n",
       " 'kebangetan',\n",
       " 'mais',\n",
       " 'dino',\n",
       " 'tuiter',\n",
       " 'karuan',\n",
       " 'dengernya',\n",
       " 'wordpress',\n",
       " 'kencan',\n",
       " 'disana',\n",
       " 'manut',\n",
       " 'bokep',\n",
       " 'dimatiin',\n",
       " 'dipikir',\n",
       " 'avatarnya',\n",
       " 'jedi',\n",
       " 'putri',\n",
       " 'kalii',\n",
       " 'youtube',\n",
       " 'dibeliin',\n",
       " 'finroll',\n",
       " 'nganggur',\n",
       " 'jugaaa',\n",
       " 'paslon',\n",
       " 'berantem',\n",
       " 'pekok',\n",
       " 'jdnya',\n",
       " 'mabok',\n",
       " 'membuatku',\n",
       " 'kerjo',\n",
       " 'kece',\n",
       " 'mbiyen',\n",
       " 'ical',\n",
       " 'folback',\n",
       " 'duitnya',\n",
       " 'dishub',\n",
       " 'elek',\n",
       " 'dibales',\n",
       " 'polresta',\n",
       " 'sukarno',\n",
       " 'woii',\n",
       " 'jamnya',\n",
       " 'liverpool',\n",
       " 'manggung',\n",
       " 'kapolsek',\n",
       " 'asoy',\n",
       " 'segitiga',\n",
       " 'tutureum',\n",
       " 'nganter',\n",
       " 'padaku',\n",
       " 'cobalah',\n",
       " 'selese',\n",
       " 'berflex',\n",
       " 'eksis',\n",
       " 'ancol',\n",
       " 'bikinnya',\n",
       " 'cemungudh',\n",
       " 'gengges',\n",
       " 'rikues',\n",
       " 'emot',\n",
       " 'kabari',\n",
       " 'unyil',\n",
       " 'ngehek',\n",
       " 'bandara',\n",
       " 'brengsek',\n",
       " ...]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('indon-words-socialmedia.json', 'w') as fopen:\n",
    "    json.dump(minus, fopen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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